Solutions to Detect and Analyze Online Radicalization : A Survey

Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organi- zations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps

Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives

We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.Comment: Accepted into ICWSM 2021, the code and datasets are publicly available at https://github.com/avalanchesiqi/youtube-crosstal

Exploiting User Comments for Audio-Visual Content Indexing and Retrieval

State-of-the-art content sharing platforms often require users to assign tags to pieces of media in order to make them easily retrievable. Since this task is sometimes perceived as tedious or boring, annotations can be sparse. Commenting on the other hand is a frequently used means of expressing user opinion towards shared media items. This work makes use of time series analyses in order to infer potential tags and indexing terms for audio-visual content from user comments. In this way, we mitigate the vocabulary gap between queries and document descriptors. Additionally, we show how large-scale encyclopaedias such as Wikipedia can aid the task of tag prediction by serving as surrogates for high-coverage natural language vocabulary lists. Our evaluation is conducted on a corpus of several million real-world user comments from the popular video sharing platform YouTube, and demonstrates signicant improvements in retrieval performance

Opinion-based Homogeneity on YouTube : Combining Sentiment and Social Network Analysis

The growing complexity of political communication online goes along with increasing methodological challenges to process communication data properly in order to investigate public concerns such as the existence of echo chambers. To cover the full range of political diversity in online communication, we argue that it is necessary to focus on specific political issues. This study proposes an innovative combination of computational methods, including natural language processing and social network analysis, that serves as a model for future research on the evolution of opinion climates in online networks. Data were gathered on YouTube, enabling the assessment of users’ expressed opinions on two political issues. Results provided very limited evidence for the existence of opinion-based homogeneity on YouTube. This was true even when the whole network was divided into sub-networks. Findings are discussed in light of current computational communication research and the vigorous debate on echo chambers in online networks

Comments on fire! Classifying flaming comments on YouTube videos in Malaysia

Flaming refers to the use of offensive language such as swearing, insulting and providing hateful comments through an online medium. In this study, the act of flaming will be explored in the context of social media, particularly YouTube. The research aims to discover the types of comments that are found on Malaysian themed YouTube videos and classify them accordingly. The Uses and Gratification theory was used as a base to explain the satisfaction obtained through YouTube as a platform to express via comments; hence obtain satisfaction through negativity. The methodology employed to carry out the study was through a content analysis. One video from the top 5 YouTube category namely entertainment, film and animation, news and politics, comedy and people and blogs were chosen with at least 100,000 views and a minimum of 100 comments. Top 100 flames were then sorted out for each video and analyzed using the thematic analysis approach. The results of this study show that the two most frequent types of comments found on Malaysian videos are political attack and racial attack. Other subcategories that are also driving the two categories mentioned above are stereotypes, speculation, comparison, degrading comments, slander/defame, sedition, sarcasm, threaten, challenge, criticism, name-calling, and sexual harassments. Through this study, the severity of the issue of flaming on account of YouTube comments has been identified; enabling the concerning party to take proper action including the use of artificial intelligence against cyber-bullying

Classifying YouTube Comments Based on Sentiment and Type of Sentence

As a YouTube channel grows, each video can potentially collect enormous amounts of comments that provide direct feedback from the viewers. These comments are a major means of understanding viewer expectations and improving channel engagement. However, the comments only represent a general collection of user opinions about the channel and the content. Many comments are poorly constructed, trivial, and have improper spellings and grammatical errors. As a result, it is a tedious job to identify the comments that best interest the content creators. In this paper, we extract and classify the raw comments into different categories based on both sentiment and sentence types that will help YouTubers find relevant comments for growing their viewership. Existing studies have focused either on sentiment analysis (positive and negative) or classification of sub-types within the same sentence types (e.g., types of questions) on a text corpus. These have limited application on non-traditional text corpus like YouTube comments. We address this challenge of text extraction and classification from YouTube comments using well-known statistical measures and machine learning models. We evaluate each combination of statistical measure and the machine learning model using cross validation and $F_1$ scores. The results show that our approach that incorporates conventional methods performs well on the classification task, validating its potential in assisting content creators increase viewer engagement on their channel.Comment: This paper was accepted at 2021 International Conference on Knowledge Discovery and Machine Learning (KDML 2021), but later withdrawn. The paper should be taken as a non peer-reviewed publicatio

Effects of Signaling on Learner Engagement in Informal Learning on YouTube

Millions of educational videos available on YouTube offer unprecedented learning opportunities. A considerable number of studies have been directed toward YouTube educational videos. Yet, research on learner engagement with YouTube educational videos is scarce, despite its central role in learning. This paper addresses this research gap. We adopt the conceptualization that learner engagement has three dimensions - behavioral, emotional, and cognitive- and investigate how signaling in YouTube educational video presentation affects learner engagement in informal learning. Our analysis shows that signaling positively affects behavioral, emotional, and cognitive engagement. These findings substantiate the empirical knowledge on learner engagement with YouTube educational videos. Our study corroborates traditional video engagement research and extends its relevance to the social media learning environment. It also informs video designers and developers on adding features in the video presentation to optimize learner engagement with YouTube educational videos